fix(simple_metrics): update adaptive state even without thresholds so removed thresholds recover (SIENTIAPDE-2191)

README.md

@@ -489,7 +489,7 @@ The **SimpleMetrics** workflow calculates regression metrics (RMSE, MSE, MAE, R
 1. **Data Loading**: Loads prediction vs target data via a JOIN query
 2. **Metrics Calculation**: Calculates configured regression metrics; alerts on `simple_metrics_thresholds` breaches (one alert per model, the message names the window) unless the window has fewer rows than `simple_metrics_min_samples`
 3. **Data Export**: Exports metrics to PostgreSQL
-4. **Adaptive State** (only with thresholds): when the schedule's `pipelines` doc has `frequency_alert`, escalates `adaptive_state` on a breach and recovers after the window is renewed without one (`min(12, ceil(interval_minutes / frequency_alert))` runs; empty or under-sampled windows count as healthy). The orchestrator runs the schedule at `frequency_alert` while escalated
+4. **Adaptive State**: when the schedule's `pipelines` doc has `frequency_alert`, escalates `adaptive_state` on a breach and recovers after the window is renewed without one (`min(12, ceil(interval_minutes / frequency_alert))` runs, at least 1; empty, under-sampled or threshold-less windows count as healthy). The orchestrator runs the schedule at `frequency_alert` while escalated
 
 #### Architecture Diagram
 ```mermaid

laborious/activities/model_metrics.py

@@ -537,7 +537,7 @@ class ModelMetrics(SientiaMonitoring):
                 - metadata (dict): Workflow metadata; `schedule_name` identifies the doc.
                 - simple_metrics (list[dict]): Rows from `calculate_simple_metrics`; empty for an
                   empty or under-sampled window, which counts as a non-breach run.
-                - thresholds (dict): `simple_metrics_thresholds` from `model_config`.
+                - thresholds (dict): `simple_metrics_thresholds` from `model_config` (may be None: no breach).
                 - interval_minutes (int): Window size, used to compute the recovery runs.
 
         Return:

laborious/workflows/simple_metrics.py

@@ -77,7 +77,7 @@ class SimpleMetrics:
         if target_data and not enough_samples:
             workflow.logger.info(
                 f'{len(target_data)} rows < simple_metrics_min_samples={min_samples}: '
-                'threshold alerting skipped'
+                'threshold alerting and escalation skipped'
             )
 
         simple_metrics = None
@@ -114,17 +114,15 @@ class SimpleMetrics:
                 start_to_close_timeout=timedelta(seconds=300),
             )
 
-        if not thresholds:
-            # No thresholds = no breach possible, so skip the Mongo round trip.
-            return
-
         try:
             await workflow.execute_activity_method(
                 Activities.update_simple_metrics_adaptive_state,
                 {
                     **metadata,
                     # Only a valid breach escalates: an empty or under-sampled window is sent as
-                    # no rows, which counts as a healthy run (spec decision 8).
+                    # no rows, which counts as a healthy run (spec decision 8). Always called, even
+                    # without thresholds (no breach), so an escalated doc recovers after they are
+                    # removed.
                     'simple_metrics': (simple_metrics or []) if enough_samples else [],
                     'thresholds': thresholds,
                     'interval_minutes': interval_minutes,

tests/laborious/workflows/test_simple_metrics.py

@@ -42,12 +42,17 @@ async def test_run(workflow_mock: AsyncMock, simple_metrics: SimpleMetrics):
     target_data = {'data': 'test_target_data'}
     simple_metrics_data = {'metrics': 'test_simple_metrics_data'}
 
-    workflow_mock.execute_activity_method = AsyncMock(side_effect=[target_data, None])
+    workflow_mock.logger = MagicMock()
+    workflow_mock.execute_activity_method = AsyncMock(side_effect=[target_data, None, None])
     workflow_mock.execute_local_activity_method = AsyncMock(return_value=simple_metrics_data)
 
     # Act
     await simple_metrics.run(input_data)
 
+    # Assert - load, export and adaptive state (no thresholds still updates it)
+    assert workflow_mock.execute_activity_method.call_count == 3
+    workflow_mock.logger.warning.assert_not_called()
+
     # Assert - Check load_custom_query call
     # Query format matches psycopg2.sql output (identifiers with double quotes, literals with single quotes)
     expected_query = f"""
@@ -130,13 +135,22 @@ async def test_run_empty_target_data(workflow_mock: AsyncMock, simple_metrics: S
 
     target_data = None
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(return_value=target_data)
+    workflow_mock.execute_local_activity_method = AsyncMock()
 
     # Act
     await simple_metrics.run(input_data)
 
-    # Assert - Should not call calculate_simple_metrics or export
+    # Assert - No calculate or export; load + adaptive state (empty window = healthy run)
-    assert workflow_mock.execute_activity_method.call_count == 1
+    workflow_mock.execute_local_activity_method.assert_not_called()
+    calls = workflow_mock.execute_activity_method.call_args_list
+    assert [c.args[0] for c in calls] == [
+        Activities.load_custom_query,
+        Activities.update_simple_metrics_adaptive_state,
+    ]
+    assert calls[1].args[1]['simple_metrics'] == []
+    workflow_mock.logger.warning.assert_not_called()
 
 
 @mark.asyncio
@@ -159,15 +173,22 @@ async def test_run_empty_simple_metrics(workflow_mock: AsyncMock, simple_metrics
     target_data = {'data': 'test_target_data'}
     simple_metrics_data = None
 
+    workflow_mock.logger = MagicMock()
     workflow_mock.execute_activity_method = AsyncMock(return_value=target_data)
     workflow_mock.execute_local_activity_method = AsyncMock(return_value=simple_metrics_data)
 
     # Act
     await simple_metrics.run(input_data)
 
-    # Assert - Should call calculate_simple_metrics but not export
+    # Assert - calculate returns None: no export, adaptive state gets [] (the `or []` is live)
-    workflow_mock.execute_activity_method.assert_called_once()
     workflow_mock.execute_local_activity_method.assert_called_once()
+    calls = workflow_mock.execute_activity_method.call_args_list
+    assert [c.args[0] for c in calls] == [
+        Activities.load_custom_query,
+        Activities.update_simple_metrics_adaptive_state,
+    ]
+    assert calls[1].args[1]['simple_metrics'] == []
+    workflow_mock.logger.warning.assert_not_called()
 
 
 @mark.asyncio
@@ -190,12 +211,17 @@ async def test_run_default_metrics(workflow_mock: AsyncMock, simple_metrics: Sim
     target_data = {'data': 'test_target_data'}
     simple_metrics_data = {'metrics': 'test_simple_metrics_data'}
 
-    workflow_mock.execute_activity_method = AsyncMock(side_effect=[target_data, None])
+    workflow_mock.logger = MagicMock()
+    workflow_mock.execute_activity_method = AsyncMock(side_effect=[target_data, None, None])
     workflow_mock.execute_local_activity_method = AsyncMock(return_value=simple_metrics_data)
 
     # Act
     await simple_metrics.run(input_data)
 
+    # Assert - all three activities ran (a swallowed exhausted side_effect would warn)
+    assert workflow_mock.execute_activity_method.call_count == 3
+    workflow_mock.logger.warning.assert_not_called()
+
     # Assert - Check calculate_simple_metrics call with default metrics
     workflow_mock.execute_activity_method.assert_any_call(
         Activities.load_custom_query,
@@ -305,13 +331,21 @@ async def test_run_updates_adaptive_state_after_export(workflow_mock, simple_met
 
 @mark.asyncio
 @patch('laborious.workflows.simple_metrics.workflow', new_callable=AsyncMock)
-async def test_run_without_thresholds_skips_adaptive_state(workflow_mock, simple_metrics):
+async def test_run_without_thresholds_still_updates_adaptive_state(workflow_mock, simple_metrics):
-    workflow_mock.execute_activity_method = AsyncMock(side_effect=[[_ROW] * 3, None])
+    # No thresholds = never a breach; still calling lets an escalated doc recover after the
+    # thresholds are removed.
+    workflow_mock.logger = MagicMock()
+    workflow_mock.execute_activity_method = AsyncMock(side_effect=[[_ROW] * 3, None, None])
     workflow_mock.execute_local_activity_method = AsyncMock(return_value=_BREACH_ROWS)
 
     await simple_metrics.run(_input())
 
-    assert workflow_mock.execute_activity_method.call_count == 2
+    calls = workflow_mock.execute_activity_method.call_args_list
+    assert len(calls) == 3
+    assert calls[2].args[0] is Activities.update_simple_metrics_adaptive_state
+    assert calls[2].args[1]['thresholds'] is None
+    assert calls[2].args[1]['simple_metrics'] == _BREACH_ROWS
+    workflow_mock.logger.warning.assert_not_called()
 
 
 @mark.asyncio